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<li><a href="./">实验设计与统计分析</a></li>

<li class="divider"></li>
<li class="chapter" data-level="" data-path="index.html"><a href="index.html"><i class="fa fa-check"></i>前言</a></li>
<li class="chapter" data-level="1" data-path="intro.html"><a href="intro.html"><i class="fa fa-check"></i><b>1</b> 实验设计概述</a><ul>
<li class="chapter" data-level="1.1" data-path="intro.html"><a href="intro.html#实验设计发展趋势"><i class="fa fa-check"></i><b>1.1</b> 实验设计发展趋势</a><ul>
<li class="chapter" data-level="1.1.1" data-path="intro.html"><a href="intro.html#实验室研究"><i class="fa fa-check"></i><b>1.1.1</b> 实验室研究</a></li>
<li class="chapter" data-level="1.1.2" data-path="intro.html"><a href="intro.html#社会研究"><i class="fa fa-check"></i><b>1.1.2</b> 社会研究</a></li>
<li class="chapter" data-level="1.1.3" data-path="intro.html"><a href="intro.html#多因素实验设计的特点"><i class="fa fa-check"></i><b>1.1.3</b> 多因素实验设计的特点</a></li>
</ul></li>
<li class="chapter" data-level="1.2" data-path="intro.html"><a href="intro.html#实验设计中的基本概念"><i class="fa fa-check"></i><b>1.2</b> 实验设计中的基本概念</a><ul>
<li class="chapter" data-level="1.2.1" data-path="intro.html"><a href="intro.html#因素与因素实验设计"><i class="fa fa-check"></i><b>1.2.1</b> 因素与因素实验设计</a></li>
<li class="chapter" data-level="1.2.2" data-path="intro.html"><a href="intro.html#处理与处理水平的结合"><i class="fa fa-check"></i><b>1.2.2</b> 处理与处理水平的结合</a></li>
<li class="chapter" data-level="1.2.3" data-path="intro.html"><a href="intro.html#主效应与交互作用"><i class="fa fa-check"></i><b>1.2.3</b> 主效应与交互作用</a></li>
<li class="chapter" data-level="1.2.4" data-path="intro.html"><a href="intro.html#简单效应"><i class="fa fa-check"></i><b>1.2.4</b> 简单效应</a></li>
<li class="chapter" data-level="1.2.5" data-path="intro.html"><a href="intro.html#处理效应和误差变异"><i class="fa fa-check"></i><b>1.2.5</b> 处理效应和误差变异</a></li>
<li class="chapter" data-level="1.2.6" data-path="intro.html"><a href="intro.html#嵌套"><i class="fa fa-check"></i><b>1.2.6</b> 嵌套</a></li>
<li class="chapter" data-level="1.2.7" data-path="intro.html"><a href="intro.html#方差分析"><i class="fa fa-check"></i><b>1.2.7</b> 方差分析</a></li>
</ul></li>
<li class="chapter" data-level="1.3" data-path="intro.html"><a href="intro.html#实验中各种变异的控制"><i class="fa fa-check"></i><b>1.3</b> 实验中各种变异的控制</a><ul>
<li class="chapter" data-level="1.3.1" data-path="intro.html"><a href="intro.html#使系统变异的效应最大"><i class="fa fa-check"></i><b>1.3.1</b> 使系统变异的效应最大</a></li>
<li class="chapter" data-level="1.3.2" data-path="intro.html"><a href="intro.html#控制无关变异"><i class="fa fa-check"></i><b>1.3.2</b> 控制无关变异</a></li>
<li class="chapter" data-level="1.3.3" data-path="intro.html"><a href="intro.html#使误差变异最小"><i class="fa fa-check"></i><b>1.3.3</b> 使误差变异最小</a></li>
</ul></li>
<li class="chapter" data-level="1.4" data-path="intro.html"><a href="intro.html#实验设计的分类"><i class="fa fa-check"></i><b>1.4</b> 实验设计的分类</a><ul>
<li class="chapter" data-level="1.4.1" data-path="intro.html"><a href="intro.html#完全随机随机区组和拉丁方实验设计"><i class="fa fa-check"></i><b>1.4.1</b> 完全随机、随机区组和拉丁方实验设计</a></li>
<li class="chapter" data-level="1.4.2" data-path="intro.html"><a href="intro.html#单因素和多因素实验设计"><i class="fa fa-check"></i><b>1.4.2</b> 单因素和多因素实验设计</a></li>
<li class="chapter" data-level="1.4.3" data-path="intro.html"><a href="intro.html#被试间被试内和混合实验设计"><i class="fa fa-check"></i><b>1.4.3</b> 被试间、被试内和混合实验设计</a></li>
</ul></li>
</ul></li>
<li class="chapter" data-level="2" data-path="onedouble.html"><a href="onedouble.html"><i class="fa fa-check"></i><b>2</b> 单组与双组实验设计</a><ul>
<li class="chapter" data-level="2.1" data-path="onedouble.html"><a href="onedouble.html#单组实验设计"><i class="fa fa-check"></i><b>2.1</b> 单组实验设计</a><ul>
<li class="chapter" data-level="2.1.1" data-path="onedouble.html"><a href="onedouble.html#单组后测实验设计"><i class="fa fa-check"></i><b>2.1.1</b> 单组后测实验设计</a></li>
<li class="chapter" data-level="2.1.2" data-path="onedouble.html"><a href="onedouble.html#单组前后测实验设计"><i class="fa fa-check"></i><b>2.1.2</b> 单组前后测实验设计</a></li>
</ul></li>
<li class="chapter" data-level="2.2" data-path="onedouble.html"><a href="onedouble.html#双组实验设计"><i class="fa fa-check"></i><b>2.2</b> 双组实验设计</a><ul>
<li class="chapter" data-level="2.2.1" data-path="onedouble.html"><a href="onedouble.html#双组前后测实验设计"><i class="fa fa-check"></i><b>2.2.1</b> 双组前后测实验设计</a></li>
<li class="chapter" data-level="2.2.2" data-path="onedouble.html"><a href="onedouble.html#双组延时实验设计"><i class="fa fa-check"></i><b>2.2.2</b> 双组延时实验设计</a></li>
</ul></li>
</ul></li>
<li class="chapter" data-level="3" data-path="basic.html"><a href="basic.html"><i class="fa fa-check"></i><b>3</b> 单因素实验设计</a><ul>
<li class="chapter" data-level="3.1" data-path="basic.html"><a href="basic.html#单因素完全随机实验设计"><i class="fa fa-check"></i><b>3.1</b> 单因素完全随机实验设计</a></li>
<li class="chapter" data-level="3.2" data-path="basic.html"><a href="basic.html#单因素随机区组实验设计"><i class="fa fa-check"></i><b>3.2</b> 单因素随机区组实验设计</a></li>
<li class="chapter" data-level="3.3" data-path="basic.html"><a href="basic.html#单因素拉丁方实验设计"><i class="fa fa-check"></i><b>3.3</b> 单因素拉丁方实验设计</a></li>
<li class="chapter" data-level="3.4" data-path="basic.html"><a href="basic.html#单因素重复测量实验设计"><i class="fa fa-check"></i><b>3.4</b> 单因素重复测量实验设计</a></li>
</ul></li>
<li class="chapter" data-level="4" data-path="towfactors.html"><a href="towfactors.html"><i class="fa fa-check"></i><b>4</b> 两因素实验设计</a><ul>
<li class="chapter" data-level="4.1" data-path="towfactors.html"><a href="towfactors.html#两因素完全随机实验设计"><i class="fa fa-check"></i><b>4.1</b> 两因素完全随机实验设计</a><ul>
<li class="chapter" data-level="4.1.1" data-path="towfactors.html"><a href="towfactors.html#前提条件"><i class="fa fa-check"></i><b>4.1.1</b> 前提条件</a></li>
<li class="chapter" data-level="4.1.2" data-path="towfactors.html"><a href="towfactors.html#统计分析思路"><i class="fa fa-check"></i><b>4.1.2</b> 统计分析思路</a></li>
<li class="chapter" data-level="4.1.3" data-path="towfactors.html"><a href="towfactors.html#spss中的实现"><i class="fa fa-check"></i><b>4.1.3</b> SPSS中的实现</a></li>
<li class="chapter" data-level="4.1.4" data-path="towfactors.html"><a href="towfactors.html#spss-分析结果的解读"><i class="fa fa-check"></i><b>4.1.4</b> SPSS 分析结果的解读</a></li>
</ul></li>
<li class="chapter" data-level="4.2" data-path="towfactors.html"><a href="towfactors.html#两因素混合实验设计"><i class="fa fa-check"></i><b>4.2</b> 两因素混合实验设计</a><ul>
<li class="chapter" data-level="4.2.1" data-path="towfactors.html"><a href="towfactors.html#两因素混合实验设计的前提条件"><i class="fa fa-check"></i><b>4.2.1</b> 两因素混合实验设计的前提条件</a></li>
<li class="chapter" data-level="4.2.2" data-path="towfactors.html"><a href="towfactors.html#两因素混合实验的基本特点"><i class="fa fa-check"></i><b>4.2.2</b> 两因素混合实验的基本特点</a></li>
<li class="chapter" data-level="4.2.3" data-path="towfactors.html"><a href="towfactors.html#分析思路"><i class="fa fa-check"></i><b>4.2.3</b> 分析思路</a></li>
<li class="chapter" data-level="4.2.4" data-path="towfactors.html"><a href="towfactors.html#两因素混合实验的spss实现"><i class="fa fa-check"></i><b>4.2.4</b> 两因素混合实验的SPSS实现</a></li>
<li class="chapter" data-level="4.2.5" data-path="towfactors.html"><a href="towfactors.html#两因素混合实验分析结果的解读"><i class="fa fa-check"></i><b>4.2.5</b> 两因素混合实验分析结果的解读</a></li>
</ul></li>
<li class="chapter" data-level="4.3" data-path="towfactors.html"><a href="towfactors.html#两因素重复测量实验设计"><i class="fa fa-check"></i><b>4.3</b> 两因素重复测量实验设计</a><ul>
<li class="chapter" data-level="4.3.1" data-path="towfactors.html"><a href="towfactors.html#两因素重复测量实验设计的前提条件"><i class="fa fa-check"></i><b>4.3.1</b> 两因素重复测量实验设计的前提条件</a></li>
<li class="chapter" data-level="4.3.2" data-path="towfactors.html"><a href="towfactors.html#两因素重复测量实验的基本特点"><i class="fa fa-check"></i><b>4.3.2</b> 两因素重复测量实验的基本特点</a></li>
<li class="chapter" data-level="4.3.3" data-path="towfactors.html"><a href="towfactors.html#两因素重复测量实验的spss数据分析"><i class="fa fa-check"></i><b>4.3.3</b> 两因素重复测量实验的SPSS数据分析</a></li>
<li class="chapter" data-level="4.3.4" data-path="towfactors.html"><a href="towfactors.html#两因素重复测量实验分析结果的解读"><i class="fa fa-check"></i><b>4.3.4</b> 两因素重复测量实验分析结果的解读</a></li>
</ul></li>
<li class="chapter" data-level="4.4" data-path="towfactors.html"><a href="towfactors.html#小结"><i class="fa fa-check"></i><b>4.4</b> 小结</a></li>
</ul></li>
<li class="chapter" data-level="5" data-path="threefactors.html"><a href="threefactors.html"><i class="fa fa-check"></i><b>5</b> 三因素实验设计</a><ul>
<li class="chapter" data-level="5.1" data-path="threefactors.html"><a href="threefactors.html#三因素完全随机实验设计"><i class="fa fa-check"></i><b>5.1</b> 三因素完全随机实验设计</a><ul>
<li class="chapter" data-level="5.1.1" data-path="threefactors.html"><a href="threefactors.html#前提条件-1"><i class="fa fa-check"></i><b>5.1.1</b> 前提条件</a></li>
<li class="chapter" data-level="5.1.2" data-path="threefactors.html"><a href="threefactors.html#统计分析思路-1"><i class="fa fa-check"></i><b>5.1.2</b> 统计分析思路</a></li>
<li class="chapter" data-level="5.1.3" data-path="threefactors.html"><a href="threefactors.html#在-spss-中的实现及结果解读"><i class="fa fa-check"></i><b>5.1.3</b> 在 SPSS 中的实现及结果解读</a></li>
</ul></li>
<li class="chapter" data-level="5.2" data-path="threefactors.html"><a href="threefactors.html#重复测量一个因素的三因素混合实验"><i class="fa fa-check"></i><b>5.2</b> 重复测量一个因素的三因素混合实验</a><ul>
<li class="chapter" data-level="5.2.1" data-path="threefactors.html"><a href="threefactors.html#重复测量一个因素的三因素混合实验的前提条件"><i class="fa fa-check"></i><b>5.2.1</b> 重复测量一个因素的三因素混合实验的前提条件</a></li>
<li class="chapter" data-level="5.2.2" data-path="threefactors.html"><a href="threefactors.html#重复测量一个因素的三因素混合实验的统计分析思路"><i class="fa fa-check"></i><b>5.2.2</b> 重复测量一个因素的三因素混合实验的统计分析思路</a></li>
<li class="chapter" data-level="5.2.3" data-path="threefactors.html"><a href="threefactors.html#在-spss-中的实现及结果解读案例"><i class="fa fa-check"></i><b>5.2.3</b> 在 SPSS 中的实现及结果解读案例</a></li>
</ul></li>
<li class="chapter" data-level="5.3" data-path="threefactors.html"><a href="threefactors.html#重复测量两个因素的三因素混合实验"><i class="fa fa-check"></i><b>5.3</b> 重复测量两个因素的三因素混合实验</a><ul>
<li class="chapter" data-level="5.3.1" data-path="threefactors.html"><a href="threefactors.html#重复测量两个因素的三因素混合实验的前提条件"><i class="fa fa-check"></i><b>5.3.1</b> 重复测量两个因素的三因素混合实验的前提条件</a></li>
<li class="chapter" data-level="5.3.2" data-path="threefactors.html"><a href="threefactors.html#重复测量两个因素的三因素混合实验的统计分析思路"><i class="fa fa-check"></i><b>5.3.2</b> 重复测量两个因素的三因素混合实验的统计分析思路</a></li>
<li class="chapter" data-level="5.3.3" data-path="threefactors.html"><a href="threefactors.html#在-spss-中的实现及结果解读案例-1"><i class="fa fa-check"></i><b>5.3.3</b> 在 SPSS 中的实现及结果解读案例</a></li>
</ul></li>
<li class="chapter" data-level="5.4" data-path="threefactors.html"><a href="threefactors.html#三因素重复测量的实验设计"><i class="fa fa-check"></i><b>5.4</b> 三因素重复测量的实验设计</a><ul>
<li class="chapter" data-level="5.4.1" data-path="threefactors.html"><a href="threefactors.html#三因素重复测量的前提条件"><i class="fa fa-check"></i><b>5.4.1</b> 三因素重复测量的前提条件</a></li>
<li class="chapter" data-level="5.4.2" data-path="threefactors.html"><a href="threefactors.html#三因素重复测量实验的统计分析思路"><i class="fa fa-check"></i><b>5.4.2</b> 三因素重复测量实验的统计分析思路</a></li>
<li class="chapter" data-level="5.4.3" data-path="threefactors.html"><a href="threefactors.html#在-spss-中的实现及结果解读案例-2"><i class="fa fa-check"></i><b>5.4.3</b> 在 SPSS 中的实现及结果解读案例</a></li>
</ul></li>
</ul></li>
<li class="chapter" data-level="" data-path="references.html"><a href="references.html"><i class="fa fa-check"></i>参考文献</a></li>
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          <h1>
            <i class="fa fa-circle-o-notch fa-spin"></i><a href="./">实验研究的设计及分析</a>
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<div id="onedouble" class="section level1">
<h1><span class="header-section-number">第 2 章</span> 单组与双组实验设计</h1>
<div id="单组实验设计" class="section level2">
<h2><span class="header-section-number">2.1</span> 单组实验设计</h2>
<p>单组实验设计是以一组被试为实验研究对象，施加某种实验处理的实验设计。可分为单组后测实验设计和单组前后测实验设计。</p>
<div id="单组后测实验设计" class="section level3">
<h3><span class="header-section-number">2.1.1</span> 单组后测实验设计</h3>
<p>单组后测实验设计的模式如下：</p>
<p><span class="math display">\[ X - O \]</span></p>
<p>其中，X代表实验处理，O代表实验的后测。这种实验模式缺乏对无关变量的控制，缺少被试实验前后测量结果的对比，又由于没有对照组，难以排除无关变量的影响。</p>
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<div id="单组前后测实验设计" class="section level3">
<h3><span class="header-section-number">2.1.2</span> 单组前后测实验设计</h3>
<p>单组后测实验设计的模式如下：</p>
<p><span class="math display">\[ O_1 - X -O_2 \]</span></p>
<p>其中，<span class="math inline">\(O_1\)</span>代表实验的前测，X代表实验处理，<span class="math inline">\(O_2\)</span>代表实验的后测。</p>
<p>单组前后测实验设计，由于增加了一次前测，提供了一个与后测结果进行比较的参照点，故而实验的内部效度在一定程度上有了提高。但这种模式仍然难以排除被试其它特征对因变量的影响。</p>
<p><strong>配对样本平均值的比较</strong></p>
<p>单组前后测的实验，在进行数据分析时，需采用配对样本t检验。</p>
<ol style="list-style-type: decimal">
<li>数据输入时，分为前测和后测；</li>
<li>先看相关系数及其显著性检验：相关系数不显著，说明两组数据相互独立，满足统计假设条件，可以进行配对平均值的差异性分析。</li>
<li>再看t检验结果，决出决策。</li>
</ol>
<p><a href="data/prepost.sav">练习数据</a></p>
<p>单组前后测实验设计，只有当实验周期较短、环境较稳定、并能明确排除被试自然成熟的影响时才可采用。</p>
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<div id="双组实验设计" class="section level2">
<h2><span class="header-section-number">2.2</span> 双组实验设计</h2>
<p>双组实验设计是一种经常采用的实验设计方法，它是通过设置一个对照组来平衡无关因素对实验结果产生的影响，因而其结论更具说服力。双组实验设计有两种模式：双组前后测实验设计和双组延时实验设计。</p>
<div id="双组前后测实验设计" class="section level3">
<h3><span class="header-section-number">2.2.1</span> 双组前后测实验设计</h3>
<p>双组前后测实验设计的模式如下：</p>
<p><span class="math display">\[ 
G_1: O_1 - X_1 - O_2 \\
G_2: O_3 - X_2 - O_4 
\]</span></p>
<p>其中，<span class="math inline">\(G_1\)</span>代表组1，<span class="math inline">\(G_2\)</span>代表组2。<span class="math inline">\(O_1\)</span>与<span class="math inline">\(O_2\)</span>分别代表组1的前测与后测，<span class="math inline">\(O_3\)</span>与<span class="math inline">\(O_4\)</span>分别代表组2的前测与后测，<span class="math inline">\(X_1\)</span>与<span class="math inline">\(X_2\)</span>表示不同的实验处理。</p>
<p>该实验设计的逻辑是：在实验前后对两组进行前测，并检验前测成绩的差异，在保证两组前测成绩没有显著性差异的基础上，两组被试分别接受不同的实验处理，实验后，对两组被试进行后测，并对两组后测成绩进行差异的显著性检验，如果通过检验，那就说明，两组的差异是由于不同的实验处理造成的。</p>
</div>
<div id="双组延时实验设计" class="section level3">
<h3><span class="header-section-number">2.2.2</span> 双组延时实验设计</h3>
<p>双组延时实验设计的模式如下：</p>
<p><span class="math display">\[ 
G_1: O_1 - X_1 - O_2 - O_3 \\
G_2: O_4 - X_2 - O_5 - O_6 
\]</span></p>
<p>其中，<span class="math inline">\(G_1\)</span>代表组1，<span class="math inline">\(G_2\)</span>代表组2。<span class="math inline">\(O_1\)</span>、<span class="math inline">\(O_2\)</span>、<span class="math inline">\(O_3\)</span>分别代表组1的前测与后测和第二次后测，<span class="math inline">\(O_4\)</span>、<span class="math inline">\(O_5\)</span>、<span class="math inline">\(O_6\)</span>分别代表组2的前测与后测和第二次后测，<span class="math inline">\(X_1\)</span>与<span class="math inline">\(X_2\)</span>表示不同的实验处理。</p>
<p>该模式主要用于验证实验处理是否具有延时效应。</p>

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